FUZZY C-MEANS with APRIORI & ID3 for PREDICTING HEART STROKE RISK LEVEL

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JAMSHEELA O Mohd Abdul Hameed Fetenech Meskele

Abstract

The past decades have brought many
remarkable researches in diagnosis of
disease. The interpretation of the problems
in medicine is a significant and tedious
task. The detection of heart problem from
various factors or symptoms is an issue
which is not free from false presumptions
often accompanied by unpredictable
effects. Thus the effort to utilize
knowledge and experience of numerous
specialists and clinical data of patients
collected earlier to facilitate the
interpretation process is considered as a
valuable asset. This paper introduces an
efficient approach to predict heart stroke
risk levels from the heart problem dataset
by using machine learning technique.
Earlier researchers have used k-means
based mafia algorithm and the accuracy
was 74%. When modifying the algorithm
with fuzzy c-means, the accuracy is
increased to 89%. There is a 15%
improvement while comparing to the
earlier algorithm.

Article Details

How to Cite
O, JAMSHEELA; HAMEED, Mohd Abdul; MESKELE, Fetenech. FUZZY C-MEANS with APRIORI & ID3 for PREDICTING HEART STROKE RISK LEVEL. INFOCOMP Journal of Computer Science, [S.l.], v. 18, n. 2, p. 01-07, dec. 2019. ISSN 1982-3363. Available at: <http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/611>. Date accessed: 20 feb. 2020.
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